Lilt

Enterprise AI translation platform combining contextual models with human verification

Hand-reviewed by the Toolglade team·Updated Aug 2026

translation#translation#localization#enterprise#human-in-the-loop

Claimed API Teams

Toolglade’s take

Lilt is a serious enterprise localization platform, not a consumer translator, and its human-in-the-loop model with adaptive contextual AI is a real differentiator for quality-sensitive content. Backing from Sequoia, Intel Capital and Redpoint and roughly $138M raised signal staying power, and the 2026 Assist agent shows it is pushing toward more autonomous workflows. The trade-offs are enterprise ones: no public pricing, a sales-led process and integration effort, so it suits organizations with sustained, high-volume translation needs rather than occasional jobs. Evaluate it on domain adaptation quality and connector fit for your stack.

About Lilt

Lilt is an enterprise AI translation and localization platform combining adaptive contextual models with human verification, connectors and workflow automation for high-volume global content.

Lilt is an enterprise-grade AI translation and localization platform built around a human-in-the-loop model rather than fully automated machine translation alone. It combines contextual AI models with a cloud-based editing environment that learns from translator input in real time, so quality improves as linguists work. The platform offers modular components including translation copilots, quality-review agents, connectors for content management systems and human verifiers, enabling organizations to run end-to-end translation workflows at scale while keeping humans in control of quality. Because it is aimed at enterprises, Lilt emphasizes secure, scalable infrastructure, deep integrations with business and enterprise systems, API access for custom applications and enterprise assurances such as SSO/SAML, high uptime and around-the-clock support. Its adaptive, feedback-driven approach is designed to reduce cost and turnaround over time as the models specialize to each customer's domain and terminology, which is particularly valuable for companies producing large volumes of multilingual content across products, support and marketing. Lilt has raised roughly $138M from investors including Sequoia Capital, Intel Capital and Redpoint Ventures, and continues to expand its agentic capabilities, including the 2026 launch of Assist, an autonomous agent for running global content programs. Pricing is not published and is quoted per customer. Lilt competes with other enterprise localization platforms and translation-management systems, differentiating on its adaptive contextual models, integrated human verification and enterprise deployment posture.

TL;DR

Lilt is an enterprise AI translation and localization platform pairing adaptive contextual models with human verification and connectors, backed by roughly $138M and expanding into agentic workflows.

Company overview

Lilt is an enterprise localization company offering an AI translation platform with human-in-the-loop quality control. It serves large organizations that need scalable, high-quality multilingual content.

The company has raised roughly $138M from investors including Sequoia Capital, Intel Capital and Redpoint Ventures, and in 2026 launched Assist, an autonomous agent for running global content programs, signaling a move toward more agentic localization.

Product features

Lilt combines contextual AI models with a cloud editing environment that learns from translator input in real time, plus translation copilots, quality-review agents, CMS connectors and human verifiers. It emphasizes enterprise security, integrations and API access.

The adaptive approach is designed to lower cost and turnaround as models specialize to a customer's domain and terminology. Modular components let organizations run end-to-end localization while keeping humans in control of quality.

Target market

Lilt targets large enterprises and global product, support and marketing teams with sustained, high-volume localization needs where quality and domain adaptation matter.